For seq2seq language models, we train LSTM LMs on the same 1000 subword units for each language.
We use a two-layer, 650-dimensional LSTM for the models trained on acoustic or semisupervised transcripts.
When adding additional LM data, we add a third layer and increase the layer sizes (850-dimension when Set 1 is added and 1024-dimension when Set1 and Set 2 are added).
The model is applied through shallow fusion [1]}.
